1,249 research outputs found
Power Management for Cloud-Scale Data Centers
Recent years have seen the rapid growth of large and geographically distributed data centers deployed by Internet service operators to support various services such as cloud computing. Consequently, high electricity bills, as well as negative environmental implications (e.g., CO2 emission and global warming) come along. In this thesis, we first propose a novel electricity bill capping algorithm that not only minimizes the electricity cost, but also enforces a cost budget on the monthly bill for cloud-scale data centers that impact the power markets. Our solution first explicitly models the impacts of the power demands induced by cloud-scale data centers on electricity prices and the power consumption of cooling and networking in the minimization of electricity bill. In the second step, if the electricity cost exceeds a desired monthly budget due to unexpectedly high workloads, our solution guarantees the quality of service for premium customers and trades off the request throughput of ordinary customers. We formulate electricity bill capping as two related constrained optimization problems and propose efficient algorithms based on mixed integer programming. We then propose GreenWare, a novel middleware system that conducts dynamic request dispatching to maximize the percentage of renewable energy used to power a network of distributed data centers, subject to the desired cost budget of the Internet service operator. Our solution first explicitly models the intermittent generation of renewable energy, e.g., wind power and solar power, with respect to varying weather conditions in the geographical location of each data center. We then formulate the core objective of GreenWare as a constrained I optimization problem and propose an efficient request dispatching algorithm based on linear-fractional programming (LFP)
Learning to Dispatch Multi-Server Jobs in Bipartite Graphs with Unknown Service Rates
Multi-server jobs are imperative in modern cloud computing systems. A
multi-server job has multiple components and requests multiple servers for
being served. How to allocate restricted computing devices to jobs is a topic
of great concern, which leads to the job scheduling and load balancing
algorithms thriving. However, current job dispatching algorithms require the
service rates to be changeless and knowable, which is difficult to realize in
production systems. Besides, for multi-server jobs, the dispatching decision
for each job component follows the All-or-Nothing property under service
locality constraints and resource capacity limits, which is not well supported
by mainstream algorithms. In this paper, we propose a dispatching algorithm for
multi-server jobs that learns the unknown service rates and simultaneously
maximizes the expected Accumulative Social Welfare (Asw). We formulate the Asw
as the sum of utilities of jobs and servers achieved over each time slot. The
utility of a job is proportional to the valuation for being served, which is
mainly impacted by the fluctuating but unknown service rates. We maximize the
Asw without knowing the exact valuations, but approximate them with
exploration-exploitation. From this, we bring in several evolving statistics
and maximize the statistical Asw with dynamic programming. The proposed
algorithm is proved to have a polynomial complexity and a State-of-the-Art
regret. We validate it with extensive simulations and the results show that the
proposed algorithm outperforms several benchmark policies with improvements by
up to 73%, 36%, and 28%, respectively
APMEC: An Automated Provisioning Framework for Multi-access Edge Computing
Novel use cases and verticals such as connected cars and human-robot
cooperation in the areas of 5G and Tactile Internet can significantly benefit
from the flexibility and reduced latency provided by Network Function
Virtualization (NFV) and Multi-Access Edge Computing (MEC). Existing frameworks
managing and orchestrating MEC and NFV are either tightly coupled or completely
separated. The former design is inflexible and increases the complexity of one
framework. Whereas, the latter leads to inefficient use of computation
resources because information are not shared. We introduce APMEC, a dedicated
framework for MEC while enabling the collaboration with the management and
orchestration (MANO) frameworks for NFV. The new design allows to reuse
allocated network services, thus maximizing resource utilization. Measurement
results have shown that APMEC can allocate up to 60% more number of network
services. Being developed on top of OpenStack, APMEC is an open source project,
available for collaboration and facilitating further research activities
Dynamic Resource Scheduling in Mobile Edge Cloud with Cloud Radio Access Network
Nowadays, by integrating the cloud radio access network (C-RAN) with the mobile edge cloud computing (MEC) technology, mobile service provider (MSP) can efficiently handle the increasing mobile traffic and enhance the capabilities of mobile devices. But the power consumption has become skyrocketing for MSP and it gravely affects the profit of MSP. Previous work often studied the power consumption in C-RAN and MEC separately while less work had considered the integration of C-RAN with MEC. In this paper, we present an unifying framework for the power-performance tradeoff of MSP by jointly scheduling network resources in C-RAN and computation resources in MEC to maximize the profit of MSP. To achieve this objective, we formulate the resource scheduling issue as a stochastic problem and design a new optimization framework by using an extended Lyapunov technique. Specially, because the standard Lyapunov technique critically assumes that job requests have fixed lengths and can be finished within each decision making interval, it is not suitable for the dynamic situation where the mobile job requests have variable lengths. To solve this problem, we extend the standard Lyapunov technique and design the VariedLen algorithm to make online decisions in consecutive time for job requests with variable lengths. Our proposed algorithm can reach time average profit that is close to the optimum with a diminishing gap (1/V) for the MSP while still maintaining strong system stability and low congestion. With extensive simulations based on a real world trace, we demonstrate the efficacy and optimality of our proposed algorithm
- …