3 research outputs found
Workload Scheduling on heterogeneous Mobile Edge Cloud in 5G networks to Minimize SLA Violation
Smart devices have become an indispensable part of our lives and gain
increasing applicability in almost every area. Latency-aware applications such
as Augmented Reality (AR), autonomous driving, and online gaming demand more
resources such as network bandwidth and computational capabilities. Since the
traditional mobile networks cannot fulfill the required bandwidth and latency,
Mobile Edge Cloud (MEC) emerged to provide cloud computing capabilities in the
proximity of users on 5G networks. In this paper, we consider a heterogeneous
MEC network with numerous mobile users that send their tasks to MEC servers.
Each task has a maximum acceptable response time. Non-uniform distribution of
users makes some MEC servers hotspots that cannot take more. A solution is to
relocate the tasks among MEC servers, called Workload Migration. We formulate
this problem of task scheduling as a mixed-integer non-linear optimization
problem to minimize the number of Service Level Agreement (SLA) violations.
Since solving this optimization problem has high computational complexity, we
introduce a greedy algorithm called MESA, Migration Enabled Scheduling
Algorithm, which reaches a near-optimal solution quickly. Our experiments show
that in the term of SLA violation, MESA is only 8% and 11% far from the optimal
choice on the average and the worst-case, respectively. Moreover, the migration
enabled solution can reduce SLA violations by about 30% compare to assigning
tasks to MEC servers without migration.Comment: 12 pages, 8 figures, 4 tables contact: hadadian AT ce DOT sharif DOT
ed
LSTM-based Traffic Load Balancing and Resource Allocation for an Edge System
The massive deployment of small cell Base Stations (SBSs) empowered with
computing capabilities presents one of the most ingenious solutions adopted for
5G cellular networks towards meeting the foreseen data explosion and the
ultra-low latency demanded by mobile applications. This empowerment of SBSs
with Multi-access Edge Computing (MEC) has emerged as a tentative solution to
overcome the latency demands and bandwidth consumption required by mobile
applications at the network edge. The MEC paradigm offers a limited amount of
resources to support computation, thus mandating the use of intelligence
mechanisms for resource allocation. The use of green energy for powering the
network apparatuses (e.g., Base Stations (BSs), MEC servers) has attracted
attention towards minimizing the carbon footprint and network operational
costs. However, due to their high intermittency and unpredictability, the
adoption of learning methods is a requisite. Towards intelligent edge system
management, this paper proposes a Green-based Edge Network Management (GENM)
algorithm, which is a online edge system management algorithm for enabling
green-based load balancing in BSs and energy savings within the MEC server. The
main goal is to minimize the overall energy consumption and guarantee the
Quality of Service (QoS) within the network. To achieve this, the GENM
algorithm performs dynamic management of BSs, autoscaling and reconfiguration
of the computing resources, and on/off switching of the fast tunable laser
drivers coupled with location-aware traffic scheduling in the MEC server. The
obtained simulation results validate our analysis and demonstrate the superior
performance of GENM compared to a benchmark algorithm.Comment: 8 Figures, 13 page
Delay Characterization of Mobile Edge Computing for 6G Time-Sensitive Services
Time-sensitive services (TSSs) have been widely envisioned for future sixth
generation (6G) wireless communication networks. Due to its inherent
low-latency advantage, mobile edge computing (MEC) will be an indispensable
enabler for TSSs. The random characteristics of the delay experienced by users
are key metrics reflecting the quality of service (QoS) of TSSs. Most existing
studies on MEC have focused on the average delay. Only a few research efforts
have been devoted to other random delay characteristics, such as the delay
bound violation probability and the probability distribution of the delay, by
decoupling the transmission and computation processes of MEC. However, if these
two processes could not be decoupled, the coupling will bring new challenges to
analyzing the random delay characteristics. In this paper, an MEC system with a
limited computation buffer at the edge server is considered. In this system,
the transmission process and computation process form a feedback loop and could
not be decoupled. We formulate a discrete-time two-stage tandem queueing
system. Then, by using the matrix-geometric method, we obtain the estimation
methods for the random delay characteristics, including the probability
distribution of the delay, the delay bound violation probability, the average
delay and the delay standard deviation. The estimation methods are verified by
simulations. The random delay characteristics are analyzed by numerical
experiments, which unveil the coupling relationship between the transmission
process and computation process for MEC. These results will largely facilitate
elaborate allocation of communication and computation resources to improve the
QoS of TSSs.Comment: 17 pages, 11 figures. This paper has been accepted by IEEE Internet
of Things Journa