6,411 research outputs found
Hierarchical Fog-Cloud Computing for IoT Systems: A Computation Offloading Game
Fog computing, which provides low-latency computing services at the network
edge, is an enabler for the emerging Internet of Things (IoT) systems. In this
paper, we study the allocation of fog computing resources to the IoT users in a
hierarchical computing paradigm including fog and remote cloud computing
services. We formulate a computation offloading game to model the competition
between IoT users and allocate the limited processing power of fog nodes
efficiently. Each user aims to maximize its own quality of experience (QoE),
which reflects its satisfaction of using computing services in terms of the
reduction in computation energy and delay. Utilizing a potential game approach,
we prove the existence of a pure Nash equilibrium and provide an upper bound
for the price of anarchy. Since the time complexity to reach the equilibrium
increases exponentially in the number of users, we further propose a
near-optimal resource allocation mechanism and prove that in a system with
IoT users, it can achieve an -Nash equilibrium in
time. Through numerical studies, we evaluate the users' QoE as well as the
equilibrium efficiency. Our results reveal that by utilizing the proposed
mechanism, more users benefit from computing services in comparison to an
existing offloading mechanism. We further show that our proposed mechanism
significantly reduces the computation delay and enables low-latency fog
computing services for delay-sensitive IoT applications
Reducing Electricity Demand Charge for Data Centers with Partial Execution
Data centers consume a large amount of energy and incur substantial
electricity cost. In this paper, we study the familiar problem of reducing data
center energy cost with two new perspectives. First, we find, through an
empirical study of contracts from electric utilities powering Google data
centers, that demand charge per kW for the maximum power used is a major
component of the total cost. Second, many services such as Web search tolerate
partial execution of the requests because the response quality is a concave
function of processing time. Data from Microsoft Bing search engine confirms
this observation.
We propose a simple idea of using partial execution to reduce the peak power
demand and energy cost of data centers. We systematically study the problem of
scheduling partial execution with stringent SLAs on response quality. For a
single data center, we derive an optimal algorithm to solve the workload
scheduling problem. In the case of multiple geo-distributed data centers, the
demand of each data center is controlled by the request routing algorithm,
which makes the problem much more involved. We decouple the two aspects, and
develop a distributed optimization algorithm to solve the large-scale request
routing problem. Trace-driven simulations show that partial execution reduces
cost by for one data center, and by for geo-distributed
data centers together with request routing.Comment: 12 page
Eco-friendly Power Cost Minimization for Geo-distributed Data Centers Considering Workload Scheduling
The rapid development of renewable energy in the energy Internet is expected
to alleviate the increasingly severe power problem in data centers, such as the
huge power costs and pollution. This paper focuses on the eco-friendly power
cost minimization for geo-distributed data centers supplied by multi-source
power, where the geographical scheduling of workload and temporal scheduling of
batteries' charging and discharging are both considered. Especially, we
innovatively propose the Pollution Index Function to model the pollution of
different kinds of power, which can encourage the use of cleaner power and
improve power savings. We first formulate the eco-friendly power cost
minimization problem as a multi-objective and mixed-integer programming
problem, and then simplify it as a single-objective problem with integer
constraints. Secondly, we propose a Sequential Convex Programming (SCP)
algorithm to find the globally optimal non-integer solution of the simplified
problem, which is non-convex, and then propose a low-complexity searching
method to seek for the quasi-optimal mixed-integer solution of it. Finally,
simulation results reveal that our method can improve the clean energy usage up
to 50\%--60\% and achieve power cost savings up to 10\%--30\%, as well as
reduce the delay of requests.Comment: 14 pages, 19 figure
Edge Intelligence: The Confluence of Edge Computing and Artificial Intelligence
Along with the rapid developments in communication technologies and the surge
in the use of mobile devices, a brand-new computation paradigm, Edge Computing,
is surging in popularity. Meanwhile, Artificial Intelligence (AI) applications
are thriving with the breakthroughs in deep learning and the many improvements
in hardware architectures. Billions of data bytes, generated at the network
edge, put massive demands on data processing and structural optimization. Thus,
there exists a strong demand to integrate Edge Computing and AI, which gives
birth to Edge Intelligence. In this paper, we divide Edge Intelligence into AI
for edge (Intelligence-enabled Edge Computing) and AI on edge (Artificial
Intelligence on Edge). The former focuses on providing more optimal solutions
to key problems in Edge Computing with the help of popular and effective AI
technologies while the latter studies how to carry out the entire process of
building AI models, i.e., model training and inference, on the edge. This paper
provides insights into this new inter-disciplinary field from a broader
perspective. It discusses the core concepts and the research road-map, which
should provide the necessary background for potential future research
initiatives in Edge Intelligence.Comment: 13 pages, 3 figure
Power Minimization Based Joint Task Scheduling and Resource Allocation in Downlink C-RAN
In this paper, we consider the network power minimization problem in a
downlink cloud radio access network (C-RAN), taking into account the power
consumed at the baseband unit (BBU) for computation and the power consumed at
the remote radio heads and fronthaul links for transmission. The power
minimization problem for transmission is a fast time-scale issue whereas the
power minimization problem for computation is a slow time-scale issue.
Therefore, the joint network power minimization problem is a mixed time-scale
problem. To tackle the time-scale challenge, we introduce large system analysis
to turn the original fast time-scale problem into a slow time-scale one that
only depends on the statistical channel information. In addition, we propose a
bound improving branch-and-bound algorithm and a combinational algorithm to
find the optimal and suboptimal solutions to the power minimization problem for
computation, respectively, and propose an iterative coordinate descent
algorithm to find the solutions to the power minimization problem for
transmission. Finally, a distributed algorithm based on hierarchical
decomposition is proposed to solve the joint network power minimization
problem. In summary, this work provides a framework to investigate how
execution efficiency and computing capability at BBU as well as delay
constraint of tasks can affect the network power minimization problem in
C-RANs
Online Learning for Offloading and Autoscaling in Energy Harvesting Mobile Edge Computing
Mobile edge computing (a.k.a. fog computing) has recently emerged to enable
in-situ processing of delay-sensitive applications at the edge of mobile
networks. Providing grid power supply in support of mobile edge computing,
however, is costly and even infeasible (in certain rugged or under-developed
areas), thus mandating on-site renewable energy as a major or even sole power
supply in increasingly many scenarios. Nonetheless, the high intermittency and
unpredictability of renewable energy make it very challenging to deliver a high
quality of service to users in energy harvesting mobile edge computing systems.
In this paper, we address the challenge of incorporating renewables into mobile
edge computing and propose an efficient reinforcement learning-based resource
management algorithm, which learns on-the-fly the optimal policy of dynamic
workload offloading (to the centralized cloud) and edge server provisioning to
minimize the long-term system cost (including both service delay and
operational cost). Our online learning algorithm uses a decomposition of the
(offline) value iteration and (online) reinforcement learning, thus achieving a
significant improvement of learning rate and run-time performance when compared
to standard reinforcement learning algorithms such as Q-learning. We prove the
convergence of the proposed algorithm and analytically show that the learned
policy has a simple monotone structure amenable to practical implementation.
Our simulation results validate the efficacy of our algorithm, which
significantly improves the edge computing performance compared to fixed or
myopic optimization schemes and conventional reinforcement learning algorithms.Comment: arXiv admin note: text overlap with arXiv:1701.01090 by other author
Proactive Demand Response for Data Centers: A Win-Win Solution
In order to reduce the energy cost of data centers, recent studies suggest
distributing computation workload among multiple geographically dispersed data
centers, by exploiting the electricity price difference. However, the impact of
data center load redistribution on the power grid is not well understood yet.
This paper takes the first step towards tackling this important issue, by
studying how the power grid can take advantage of the data centers' load
distribution proactively for the purpose of power load balancing. We model the
interactions between power grid and data centers as a two-stage problem, where
the utility company chooses proper pricing mechanisms to balance the electric
power load in the first stage, and the data centers seek to minimize their
total energy cost by responding to the prices in the second stage. We show that
the two-stage problem is a bilevel quadratic program, which is NP-hard and
cannot be solved using standard convex optimization techniques. We introduce
benchmark problems to derive upper and lower bounds for the solution of the
two-stage problem. We further propose a branch and bound algorithm to attain
the globally optimal solution, and propose a heuristic algorithm with low
computational complexity to obtain an alternative close-to-optimal solution. We
also study the impact of background load prediction error using the theoretical
framework of robust optimization. The simulation results demonstrate that our
proposed scheme can not only improve the power grid reliability but also reduce
the energy cost of data centers
Fogbanks: Future Dynamic Vehicular Fog Banks for Processing, Sensing and Storage in 6G
Fixed edge processing has become a key feature of 5G networks, while playing
a key role in reducing latency, improving energy efficiency and introducing
flexible compute resource utilization on-demand with added cost savings.
Autonomous vehicles are expected to possess significantly more on-board
processing capabilities and with improved connectivity. Vehicles continue to be
used for a fraction of the day, and as such there is a potential to increase
processing capacity by utilizing these resources while vehicles are in
short-term and long-term car parks, in roads and at road intersections. Such
car parks and road segments can be transformed, through 6G networks, into
vehicular fog clusters, or Fogbanks, that can provide processing, storage and
sensing capabilities, making use of underutilized vehicular resources. We
introduce the Fogbanks concept, outline current research efforts underway in
vehicular clouds, and suggest promising directions for 6G in a world where
autonomous driving will become commonplace. Moreover, we study the processing
allocation problem in cloud-based Fogbank architecture. We solve this problem
using Mixed Integer Programming (MILP) to minimize the total power consumption
of the proposed architecture, taking into account two allocation strategies,
single allocation of tasks and distributed allocation. Finally, we describe
additional future directions needed to establish reliability, security,
virtualisation, energy efficiency, business models and standardization
Multi-Antenna NOMA for Computation Offloading in Multiuser Mobile Edge Computing Systems
This paper studies a multiuser mobile edge computing (MEC) system, in which
one base station (BS) serves multiple users with intensive computation tasks.
We exploit the multi-antenna non-orthogonal multiple access (NOMA) technique
for multiuser computation offloading, such that different users can
simultaneously offload their computation tasks to the multi-antenna BS over the
same time/frequency resources, and the BS can employ successive interference
cancellation (SIC) to efficiently decode all users' offloaded tasks for remote
execution. We aim to minimize the weighted sum-energy consumption at all users
subject to their computation latency constraints, by jointly optimizing the
communication and computation resource allocation as well as the BS's decoding
order for SIC. For the case with partial offloading, the weighted sum-energy
minimization is a convex optimization problem, for which an efficient algorithm
based on the Lagrange duality method is presented to obtain the globally
optimal solution. For the case with binary offloading, the weighted sum-energy
minimization corresponds to a {\em mixed Boolean convex problem} that is
generally more difficult to be solved. We first use the branch-and-bound (BnB)
method to obtain the globally optimal solution, and then develop two
low-complexity algorithms based on the greedy method and the convex relaxation,
respectively, to find suboptimal solutions with high quality in practice. Via
numerical results, it is shown that the proposed NOMA-based computation
offloading design significantly improves the energy efficiency of the multiuser
MEC system as compared to other benchmark schemes. It is also shown that for
the case with binary offloading, the proposed greedy method performs close to
the optimal BnB based solution, and the convex relaxation based solution
achieves a suboptimal performance but with lower implementation complexity.Comment: 33 pages, 12 figures, as well as correcting the typos in equations
(4) and (5) in the previous versio
Dynamic resource management in Cloud datacenters for Server consolidation
Cloud resource management has been a key factor for the cloud datacenters
development. Many cloud datacenters have problems in understanding and
implementing the techniques to manage, allocate and migrate the resources in
their premises. The consequences of improper resource management may result
into underutilized and wastage of resources which may also result into poor
service delivery in these datacenters. Resources like, CPU, memory, Hard disk
and servers need to be well identified and managed. In this Paper, Dynamic
Resource Management Algorithm(DRMA) shall limit itself in the management of CPU
and memory as the resources in cloud datacenters. The target is to save those
resources which may be underutilized at a particular period of time. It can be
achieved through Implementation of suitable algorithms. Here, Bin packing
algorithm can be used whereby the best fit algorithm is deployed to obtain
results and compared to select suitable algorithm for efficient use of
resources.Comment: 8 pages, 4 figure
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