1
Service Virtualization using a non-von
Neumann Parallel, Distributed & Scalable
Computing Model
Dr. Rao Mikkilineni, Member, IEEE

Abstract— That the computing models of living organisms
utilize sophisticated methods of information processing, was
recognized by von Neumann who proposed the stored program
control (SPC) computing model and the self-replicating cellular
automata. Later Chris Langton created computer programs that
demonstrated self-organization and discovery of patterns using
evolutionary rules which led to the field of artificial life and
theories of complexity.
In this paper, we focus on another aspect that we learn from
the genes in living organisms that deals with precise replication
and execution of encapsulated DNA sequences. We describe a
computing model, recently proposed, extending the SPC model to
create self-configuring, self-monitoring, self-healing, selfprotecting and self-optimizing (self-managing or self-*)
distributed software systems. This approach allows utilizing a
parallel distributed computing model to implement service
virtualization and workflow execution practiced in current
business process implementation in IT where a workflow is
implemented as a set of tasks, arranged or organized in a directed
acyclic graph (DAG).
Two implementations of the new
computing model have been realized.
Index Terms— Distributed Computing, Parallel Computing,
Distributed Services Management, Service Virtualization
I. INTRODUCTION
N his article on “The Trouble with Multi-core”, David
Patterson [1] observers that “the most optimistic outcome, of
course, is that someone figures out how to make dependable
parallel software that works efficiently as the number of cores
increases. That will provide the much-needed foundation for
building the microprocessor hardware of the next 30 years.
Even if the routine doubling every year or two the number of
transistors per chip were to stop--the dreaded end of Moore's
Law--innovative packaging might allow economical systems to
be created from multiple chips, sustaining the performance
gains that consumers have long enjoyed."
Although I'm rooting for this outcome--and many colleagues
and I are working hard to realize it--I have to admit that this
I
Manuscript received Jun 20, 2011.
R. Mikkilineni. is with Kawa Objects Inc., Los Altos, CA 94022 USA
(phone: 408-406-7639; fax: 408-517-1421; e-mail: rao@ kawaobjects.com).
third scenario is probably not the most likely one."
Up to now, the upheaval in hardware brought about by the
multi-core chips (often dubbed as an inflection point) is not
matched by an equal innovation in software to take advantage
of the abundance of computing, memory, network, and storage
resources. While the new class of processors offers parallel
processing and multi-thread architecture, the operating systems
that have evolved over the past four decades are optimized to
work with serial von Neumann stored program computers.
The term "von Neumann bottleneck" was coined by John
Backus [2] in his 1977 ACM Turing award lecture to address
the issues arising from the separation between the CPU and
memory. According to Backus: "Surely there must be a less
primitive way of making big changes in the store than by
pushing vast numbers of words back and forth through the von
Neumann bottleneck. Not only is this tube a literal bottleneck
for the data traffic of a problem, but, more importantly, it is an
intellectual bottleneck that has kept us tied to word-at-a-time
thinking instead of encouraging us to think in terms of the
larger conceptual units of the task at hand. Thus programming
is basically planning and detailing the enormous traffic of
words through the von Neumann bottleneck and much of that
traffic concerns not significant data itself, but where to find it."
The limitations of the SPC computing architecture were
clearly on his mind when von Neumann gave his lecture at the
Hixon symposium in 1948 in Pasadena, California [3]. "The
basic principle of dealing with malfunctions in nature is to
make their effect as unimportant as possible and to apply
correctives, if they are necessary at all, at leisure. In our
dealings with artificial automata, on the other hand, we require
an immediate diagnosis. Therefore, we are trying to arrange
the automata in such a manner that errors will become as
conspicuous as possible, and intervention and correction
follow immediately." Comparing the computing machines and
living organisms, he points out that the computing machines
are not as fault tolerant as the living organisms. He goes on to
say "It's very likely that on the basis of philosophy that every
error has to be caught, explained, and corrected, a system of
the complexity of the living organism would not run for a
millisecond."
The resiliency of biological systems stems from its genetic
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computing model supporting the genetic transactions of
replication, repair, recombination and reconfiguration [4].
Evolution of living organisms has taught us that the difference
between survival and extinction is the information processing
ability of the organism to:
1. Discover and encapsulate the sequences of stable patterns
that have lower entropy, which allow harmony with the
environment providing the necessary resources for its
survival,
2. Replicate the sequences so that the information (in the
form of best practices) can propagate from the survived
to the successor,
3. Execute with precision the sequences to reproduce itself,
4. Monitor itself and its surroundings in real-time, and
5. Utilize the genetic transactions of repair, recombination
and rearrangement to sustain existing patterns that are
useful.
Life’s purpose, it seems, is to transfer the stable patterns that
have proven useful from the survived to the successor in the
form of encapsulated executable best practices. A cellular
organism’s genetic program (specifying the sequences of
stable patterns that assist in establishing equilibrium between
the organism and its surroundings that provide the necessary
resources) is encoded in its DNA. Consequently, faithful
replication of that sequence is essential to preserve the
organism’s unique characteristics.
A cellular organism’s genetic program (specifying the
sequences of stable patterns that assist in establishing
equilibrium between the organism and its surroundings that
provide the necessary resources) is encoded in its DNA.
Consequently, faithful replication of that sequence is essential
to preserve the organism’s unique characteristics. According
to Singer and Berg [4], “One of the wonders of DNA is that it
encodes the complete machinery and instructions for its own
duplication: some genes code for enzymes that synthesize the
nucleotide precursors of DNA and others specify proteins that
assemble the activated nucleotides into polynucleotide chains.
There are also genes for coordinating the replication process
with other cellular events, and still others that encode the
proteins that package DNA into chromatin.
Another
extraordinary property of DNA is that it functions as a
template and directs the order in which the nucleotides are
assembled into new DNA chains. Provided with precisely the
same synthetic machinery, different DNA’s direct only the
formation of replicas of themselves.”
In addition, the genetic program also specifies the enzymatic
machinery that rectifies the errors that occur occasionally
during DNA replication, as well as enzymes that repair
damage to the bases or helical structures of DNA caused by
various factors. Even more impressive fact is that the genetic
program also provides opportunities for genome variation and
evolutionary change [4]. “Certain genes encode proteins that
promote strand exchanges between DNA molecules and
thereby create new combinations of genetic information for the
progeny. Other proteins cause genome rearrangements by
catalyzing the translocations of small segments or even large
regions within and among DNA molecules.
Such
recombination and translocations provide some of the
substrates for evolution’s experiments, but some
rearrangements cause disease. By contrast, the proper
functioning of several genetic programs actually depends on
specific DNA rearrangements.”
In essence, the genetic transactions of DNA (namely the
mechanisms of replication, repair, recombination and
rearrangement) provide a model for powerful abstractions that
are essential for a computing model to create self-configuring,
self-monitoring, self-protecting, self-healing, self-optimizing
and self-propagating distributed systems.
In this paper, based on the lessons from the von Neumann
computing model, and the genetic computing model, we
propose a new approach that implements the description,
replication, signaling enabled control and execution of
distributed tasks using the conventional SPC model. We call
this Distributed Intelligent Managed Element (DIME) network
computing model. The new computing DIME network
architecture (computing DNA) allows us to implement a
workflow as a set of tasks, arranged or organized in a directed
acyclic graph (DAG) and executed by a managed network of
distributed computing elements (DIMEs). These tasks,
depending on user requirements are programmed and executed
as loadable modules in each DIME.
In Section II, we describe the new parallel distributed
computing model and illustrate its self-* properties. In Section
III, we review two proofs of concept implementing the DIME
network architecture and sketch a possible virtual services
infrastructure. In Section IV, we present a comparison of
DIME networks with current service architectures and
conclusions for future direction.
II. THE DIME NETWORK ARCHITECTURE
The raison d’etre for DIME computing model is to fully
exploit the parallelism, distribution and massive scaling
possible with multicore processor based servers, laptops and
mobile devices supporting hardware assisted virtualization and
create a computing architecture in which the services and their
management in real-time are decoupled from the hardware
infrastructure and its management.
The model lends itself to be implemented i) from scratch to
exploit the many core servers and ii) in current generation
servers exploiting features available in current operating
systems. In this section, we describe the DIME network
architecture and both proof-of-concept implementations to
demonstrate its feasibility. Figure 1 shows the transition from
the current SPC computing model to DIME network
computing model.
The services and their management are both part of the
service executable packages implemented in a network of vonNeumann SPC computing nodes. Following the genetic
computing model, the DIME computing model separates the
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service execution and its management exploiting the
parallelism. Each DIME is a self-managed element with
autonomy on its resources and is network aware with a
signaling infrastructure. A signaling network overlay allows
parallelism in resource configuration, monitoring, analysis and
reconfiguration on-the-fly based on workload variations,
business priorities and latency constraints of the distributed
software components.
Figure 1: von Neumann SPC computing model and the
DIME computing Model
The DIME network architecture consists of four
components:
1. A DIME node which encapsulates the von Neumann
computing element with self-management of fault,
configuration, accounting, performance and security
(FCAPS).
2. Signaling capability that allows intra-DIME and InterDIME communication and control,
3. An infrastructure that allows implementing distributed
service workflows as a set of tasks, arranged or
organized in a DAG and executed by a managed
network of DIMEs and
4. An infrastructure that assures DIME network
management using the signaling network overlay over
the computing workflow
The self-management and task execution (using the DIME
component called MICE, the managed intelligent computing
element) are performed in parallel using the stored program
control computing devices. Figure 2 shows the anatomy of a
DIME.
Figure 2: The Anatomy of a DIME with Service Regulator and Service Package Executables
The self-management and task execution (using the DIME
component called MICE, the managed intelligent computing
element) are performed in parallel using the stored program
control computing devices.
The DIME orchestration template provides the description
for instantiating the DIME using an SPC computing device
with appropriate resources required (CPU, memory, network
bandwidth, storage capacity, throughput and IOPs). The
description contains the resources required, the constraints and
the addresses of executable modules for various components
and various run time commands the DIME obeys. This
description is called the regulatory gene and contains all the
information required to instantiate the DIME with its FCAPS
management components, the MICE and the signaling
framework to communicate with external DIME infrastructure.
Figure 3: The Service Regulator and The Service Package
Executables and the DIME components
The service regulator provides the description for
instantiating the DIME services using the MICE with
appropriate resources required (CPU, memory, network
bandwidth, storage capacity, throughput and IOPs). The
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description contains the resources required, the constraints and
the addresses of executable modules for various components
and various run time commands the service obeys. The
configuration commands provide the ability for the MICE to
be set up with appropriate resources and I/O communication
network to be set up to communicate with other DIME
components to become a node in a service delivery network
implementing a workflow. Figure 3 shows the service
implementation with a service regulator and the service
execution package.
Signaling allows groups of DIMEs to collaborate with each
other and implement global policies.
The signaling
abstractions are:
1. Addressing: For network based collaboration, each
FCAPS aware DIME must have a globally unique
address and any services platform using DIMEs must
provide name service management.
2. Alerting:
Each DIME
is capable of self identification, heartbeat broadcast, and provide a
published alerting interface that describes various
alerting attributes and its own FCAPS management
3. Supervision: Each DIME is a member of a network
with a purpose and role. The FCAPS interfaces are
used to define and publish the purpose, role and
various specialization services that the DIME provides
as a network community member. Supervision allows
contention resolution based on roles and purpose.
Supervision also allows policy monitoring and control.
4. Mediation: When the DIMEs are contending for
resources to accomplish their specific mission, or
require prioritization of their activities, the supervision
hierarchy is assisted with mediation object network
that provides global policy enforcement.
Figure 5: DIME Replication
By defining service S2 to execute itself, we replicate S2
DIME. Note that S2 is a service that can be programmed to
terminate instantiating itself further when resources are not
available. In addition, dynamic FCAPS (parallel service
monitoring and control) management allows changing the
behavior of any instance from outside (using the signaling
infrastructure) to alter the service that is executed. Figure 6
shows dynamic service reconfiguration.
The ability to execute the control commands in parallel
allows dynamic replacement of services during run time. For
example by stopping service S2 and loading and executing
service S1, we dynamically change the service during run time.
We can also redirect I/O dynamically during run time. Any
DIME can also allow a subnetwork instantiation and control as
shown in figure 7. The workflow orchestrator instantiates the
worker nodes, monitors heartbeat and performance of workers
and implement fault tolerance, recovery, and performance
management policies.
The DIME network architecture supports the genetic
transactions of replication, repair, recombination and
rearrangement. Figure 4 shows a single node execution of a
service in a DIME network.
Figure 6: Dynamic Service Replication & Reconfiguration
It can also implement accounting and security monitoring
and management using the signaling channel. Redirection of
I/O allows dynamic reconfiguration of worker input and output
thus providing computational network control.
Figure 4: Single node execution of a DIME
A single node of a DIME that can execute a workflow by
itself or by instantiating a sub-network provides a way to
implement a managed DAG executing a workflow.
Replication is implemented by executing the same service as
shown in figure 5.
Figure 7:
DIME Sub-network Implementing Service
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Composition, Fault & Performance Managements
III. PROOF OF CONCEPT IMPLEMENTATIONS AND VIRTUAL
SERVICES INFRASTRUCTURE
In summary, the dynamic configuration at DIME node
level and the ability to implement at each node, a managed
directed acyclic graph using a DIME sub-network provides a
powerful paradigm for designing and deploying managed
services. The DIME network computing model just formalizes
a distributed object network implementation (borrowing
heavily from the computing models deployed by the DNA and
the Genomes) that can be programmed to self-configure, selfsecure self-monitor, self-heal and self-optimize based on
business priorities, workload variations and latency constraints
implemented as local and global policies. The infrastructure
can be implemented using any of the standard Operating
Systems that are available today. The key abstractions that are
leveraged in this model are:
1. Parallel implementation of self-management and computing
element at the (DIME) node level and
2. Parallel implementation of signaling based DIME network
management and workflow implementation as a managed
DAG
The parallelism and signaling allow the dynamism required
to implement the genetic transactions which provide the self-*
features that are the distinguishing characteristics of living
organisms:
1. Specialization: each computing entity is specialized to
perform specific tasks. The intelligence is embedded
locally that can be utilized to perform collection,
computing and control functions with an analog
interface to the real world.
2. Separation of concerns: groups of computing entities
combine their specializations through mediation to
create value added services
3. Priority based mediation: the mediation is supervised
to resolve contention for resources based on overall
group objectives to optimize resource utilization
4. Fault tolerance, security and reliability: using alerting,
supervision and mediation, implement sequencing of
workflow to provide a high degree of resilience to the
workflow.
The DIME computing model does not replace any of the
computing models that are implemented using the SPC
computing model today. It provides a self-* infrastructure to
implement them with the dynamism and resiliency of living
organisms. The DIME computing model focuses only on the
reliable execution of stable patterns that are described as
managed DAGs. It does not address how to discover more
stable patterns from existing workflows (with lower entropy).
The DIME network computing model was originally
suggested by Rao Mikkilineni [5] and two proofs of concept
were developed, one using Ubuntu* Linux Operating System
[6] by Giovanni Morana and another using a native Parallax
Operating System running on Intel multi-core servers [7 and 8]
by Ian Seyler. The Linux* implementation demonstrates
scaling and self-repair of Linux* processes without the use of
Hypervisor. The Parallax OS is implemented in assembler
language with C/C++ API for higher level programming and
demonstrates scaling and self-repair across Intel multi-core
servers.
The objective of operating systems and programming
languages is to reduce the semantic gap between business
workflow definitions and their executions in a von Neumann
computing device. The important consequences of current
upheaval in hardware with multi-CPU and multi-core
architectures on a monolithic OS that shares data structures
across cores are well articulated by Baumann et al [9]. They
also introduce the need for making the OS structure, hardwareneutral. "The irony is that hardware is now changing faster
than software, and the effort required to evolve such operating
systems to perform well on new hardware is becoming
prohibitive." They argue that single computers increasingly
resemble networked systems, and should be programmed as
such.
As the number of cores increase to hundreds and thousands
in the next decade, current generation operating systems cease
to scale and full-scale networking architecture has to be
brought inside the server. The DIMEs enable the execution of
distributed and managed workflows within a server or across
multiple servers with its unifying network computing model.
Exploiting this, Parallax operating system leverages chip-level
hardware assistance provided to virtualize, manage, secure,
and optimize computing at the core. While this work is in its
infancy, the new OS also seems to exploit fully the parallelism
and multithread execution capabilities offered in these
computing elements to implement the managed DAGs with
parallel signaling control network. The service infrastructure
we sketch here has three main components:
1. Service component development platform which allows
defining each service gene (regulatory and domain
specific components),
2. Service workflow orchestrator that composes workflows
and prepares the images for runtime, and
3. Service assurance platform that allows run-time policy
implementations and dynamic services management.
Figure 8 shows a simple service domain in which a set of
distributed nodes that control environment using monitoring of
sensors and control of a fan.
There are two management workflows shown in the picture:
1. The FCAPS management of the DIME infrastructure
implemented at the operating system level and
2. The FCAPS management of the domain specific
workflow implemented at the application level
which in this case is monitoring and managing the
service workflow implemented by the MICE
network.
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Figure 8: A Service Creation, Delivery and Assurance Framework using Fault, Configuration, Accounting, Performance,
Security Management and Signaling Based Parallel, Distributed Computing Model
The global and local policies for the service domain are
implemented in each node.
The DIME infrastructure
management assures instantiation and run time-service
assurance. The framework provides a scalable architecture
with dynamic reconfiguration of service workflow made
possible by the genetic transactions of replication, repair,
recombination and reconfiguration provided in the DNA. The
implementation of the service framework either on a native OS
such as Parallax or a current OS such as Linux* or Windows*
provides a 100% decoupling of services management from
infrastructure management of hardware that is hosting the
services. The resulting architectural resilience, in our IT
infrastructure at the core, comparable to that of cellular
organisms brings telecom grade trust to global communication,
collaboration, and commerce at the speed of light.
IV. CONCLUSION
This and other papers implementing proofs of concept [5, 6,
7 and 8] describe a first step in evaluating a new parallel,
distributed and scalable computing model that extends the
current von Neumann SPC computing model. In fact, it is
perhaps closer to the self-replicating model von Neumann was
seeking to duplicate the characteristics of fault tolerance, selfhealing and other such attributes observed in living organisms
[10]. Discussing the work of Francois Jacob and Jacques
Monod on genetic switches and gene signaling, Mitchell
Waldrop [11] points out that "DNA residing in a cell's nucleus
was not just a blueprint for the cell - a catalog of how to make
this protein or that protein. DNA was actually the foreman in
charge of construction. In effect, DNA was a kind of
molecular-scale computer that directed how the cell was to
build itself and repair itself and interact with the outside
world.”
The DIME network architectures provides a way to create a
blueprint for the business workflow and a mechanism to
execute it based on a service management workflow
implementing local and global policies based on business
priorities, workload fluctuations and latency constraints. This
approach is quite distinct from current approaches [9, 12, 13,
14, 15, 16, 17 and 18] that use von-Neumann computing
model for service management and offers many new directions
of research to provide next level of scaling, telecom grade trust
through end-to-end service FCAPS optimization and reduced
complexity in developing, deploying and managing distributed
federated software systems executing business workflows.
The beauty of this model is that it does not impact the
current implementation of the service workflow using vonNeumann SPC nodes. But by introducing parallel control and
management of the service workflow, the DIME network
architecture provides the required scaling, agility and
resilience both at the node level and at the network level. The
signaling based network level control of a service workflow
that spans across multiple nodes allows the end-to-end
connection level quality of service management independent of
the hardware infrastructure management systems that do not
provide any meaningful visibility or control to the end-to-end
service transaction implementation at run time. The only
requirement for the DIME infrastructure provider is to assure
that the node OS provides the required services for the service
controller to load the Service Regulator and the Service
Execution Packages to create and execute the DIME.
The network management of DIME services allows
hierarchical scaling using the network composition of subnetworks. Each DIME with its autonomy on local resources
through FCAPS management and its network awareness
through signaling can keep its own history to provide
negotiated services to other DIMEs thus enabling a
collaborative workflow execution.
We identify various major areas of future research that may
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prove most effective:
1. Implementing DNA in current operating systems, as the
DIMEs in Linux* [6] approach illustrates, provides an
immediate path to enhance efficiency of communication
between multiple images deployed in a many-core server
without any disruption to existing applications. In addition,
auto-scaling, performance optimization, end-to-end transaction
security and self-repair attributes allow various applications
currently running under Linux* or Windows* to migrate easily
to more efficient operating platforms.
2. Implementing a new OS such as Parallax [7. 8] allows
designing a new class of scalable, and self-* distributed
systems design transcending physical, geographical and
enterprise boundaries with true decoupling between services
and the infrastructure that they reside on. The service creation
and workflow orchestration platforms can be implemented on
current generation development environments whereas the run
time services deployment and management can be orchestrated
in many-core servers with DNA.
3. Signaling and FCAPS management implemented in
hardware to design a new class of storage will allow the design
of next generation IT hardware infrastructure with Self-*
properties.
4. As hundreds of cores in a single chip enable thousands of
cores in a server, the networking infrastructure and associated
management software including routing, switching and firewall
management will migrate to the server inside from outside.
The DIME network architecture with its connection FCAPS
management using signaling control will eliminate the need to
replicate current network management infrastructure also
inside the server. The routing and switching abstractions will
be
incorporated
in intra-DIME
and
Inter-DIME
communication and signaling infrastructure.
Eventually, it is possible to conceive of signaling being
incorporated in the many-core chip itself to leverage the DNA
in hardware.
[3]
[4]
[5]
[6]
[7]
[8]
[9]
[10]
[11]
[12]
[13]
[14]
[15]
[16]
ACKNOWLEDGMENT
The author wishes to acknowledge many valuable
discussions, with Kumar Malavalli, Albert Comparini and
Vijay Sarathy from Kawa Objects Inc.*, which have
contributed to the DIME Network Architecture. The author
also wishes to express his gratitude to Giovanni Morana from
Catania University*, Italy and Ian Seyler from Return
Infinity*, Canada for very quickly implementing the DIME
networks to create the proofs of concept demonstration of
service virtualization and real-time dynamic self-* capabilities.
REFERENCES
[1]
[2]
David Patterson, “The trouble with multi-core”, IEEE Spectrum, July
2010, p28
Backus, J. “Can programming be liberated from the von Neumann
style? A functional style and its algebra of programs”, Communications
of the ACM 21, 8, (August 1978), 613-641
[17]
[18]
Neumann, J. v., "Papers of John von Neumann on Computing and
Computer Theory", in Charles Babbage Institute Reprint Series for the
History of Computing, edited by William Aspray and Arthur Burks MIT
Press.Cambridge, MA:1987, p409, p.474.
Maxine Singer and Paul Berg, “Genes & genomes: a changing
perspective”, University Science Books, Mill Valley, CA, 1991, p 73
Mikkilineni, R “Is the Network-centric Computing Paradigm for
Multicore, the Next Big Thing?” Retrieved July 22, 2010, from
Convergence of Distributed Clouds, Grids and Their Management:
http://computingclouds.wordpress.com
Giovanni Morana, and Rao Mikkilineni, “Scaling and Self-repair of
Linux* Based Applications Using a Novel Distributed Computing
Model Exploiting Parallelism". IEEE proceedings, WETICE2011, Paris,
2011
Rao Mikkilineni and Ian Seyler, "Parallax – A New Operating System
for Scalable, Distributed, and Parallel Computing", The 7th
International Workshop on Systems Management Techniques,
Processes, and Services, Anchorage, Alaska, May 2011
Rao Mikkilineni and Ian Seyler, “Parallax – A New Operating System
Prototype Demonstrating Service Scaling and Self-Repair in Multi-core
Servers”, IEEE proceedings, WETICE2011, Paris, 2011
Andrew Baumann, Paul Barham, Pierre-Evariste Dagand, Tim Harris,
Rebecca Isaacs, Simon Peter, Timothy Roscoe, Adrian Schupbach, and
Akhilesh Singhania, "The Multikernel: A new OS architecture for
scalable multicore systems", In Proceedings of the 22nd ACM
Symposium on OS Principles, Big Sky, MT, USA, October 2009
John von Neumann, Papers of John von Neumann on Computing and
Computing Theory, Hixon Symposium, September 20, 1948, Pasadena,
CA, The MIT Press, 1987, p454, p457
Mitchell Waldrop, M., “Complexity: The Emerging Science at the Edge
of Order and Chaos”, Simon and Schuster Paperback, New York, 1992,
p 31
Wentzlaff, D. and Agarwal, A. (2009). Factored operating systems (fos):
the case for a scalable operating system for multicores. SIGOPS Oper.
Syst. Rev., 43(2):76–85.
Liu, R. Klues, K. Bird, S. Hofmeyr, S. Asanovi´c, K. and Kubiatowicz,
J. (2009) Tesselation: Space-Time Partitioning in a Manycore Client
OS, In HotPar09, Berkeley, CA, 03/2009.
Colmenares, J. A. Bird, S. Cook, H. Pearce, P. Zhu, D. Shalf, J.
Hofmeyr, S. Asanovic, K and Kubiatowicz, J. (2010). Tesselation:
Space-Time Partitioning in a Manycore Client OS in Proc. 2nd USENIX
Workshop on Hot Topics in Parallelism (HotPar'10). Berkeley, CA,
USA. June.
Wentzlaff D. and Agarwal. A. (2009). Factored operating systems (fos):
the case for a scalable operating system for multicores. SIGOPS Oper.
Syst. Rev., 43(2):76–85.
Nightingale, E. B. Hodson, O. McIlroy, R. Hawblitzel, C. and Hunt G.
Helios: Heterogeneous Multiprocessing with Satellite Kernels, ACM,
SOSP’09, October 11–14, 2009, Big Sky, Montana, USA
Mao, O. Kaashoek, F. Morris, R. Pesterev, A. Stein, L. Wu, M. Dai, Y.
Zhang, Y. Zhang, Z. Corey: an operating system for many cores, (2008).
Proceedings of the 8th USENIX Symposium on Operating Systems
Design and Implementation OSDI '08, San Diego, California,
December.
Rajkumar Buyyaa, C. S. (2009). Cloud computing and emerging IT
platforms: Vision, hype, and reality for delivering computing as the 5th
utility. Future Generation Computer Systems Volume 25, Issue 6, 599616.
R. Mikkilineni received his PhD from University of California, San Diego
in 1972 working under the guidance of prof. Walter Kohn. He later worked
as a research associate in University of Paris, Orsay, Courant Institute of
Mathematical Sciences, New York and Columbia University, New York..
He is currently the Founder and CTO of Kawa Objects Inc., California,
a Silicon Valley startup developing next generation computing infrastructure.
His past experience includes working at AT&T Bell Labs, Bellcore, U S
West, several startups and more recently at Hitachi Data Systems..
Dr. Mikkilineni co-chairs the 1st track on Convergence of Distributed
Clouds, Grids and their Management in IEEE International WETICE2011
Conference.