1,303 research outputs found
Scheduling soft real-time jobs over dual non-real-time servers
In this paper, we consider soft real-time systems with redundant off-the-shelf processing components (e.g., CPU, disk, network), and show how applications can exploit the redundancy to improve the system's ability of meeting response time goals (soft deadlines). We consider two scheduling policies, one that evenly distributes load (Balance), and one that partitions load according to job slackness (Chop). We evaluate the effectiveness of these policies through analysis and simulation. Our results show that by intelligently distributing jobs by their slackness amount the servers, Chop can significantly improve real-time performance. ©1996 IEEE.published_or_final_versio
TimeTrader: Exploiting Latency Tail to Save Datacenter Energy for On-line Data-Intensive Applications
Datacenters running on-line, data-intensive applications (OLDIs) consume
significant amounts of energy. However, reducing their energy is challenging
due to their tight response time requirements. A key aspect of OLDIs is that
each user query goes to all or many of the nodes in the cluster, so that the
overall time budget is dictated by the tail of the replies' latency
distribution; replies see latency variations both in the network and compute.
Previous work proposes to achieve load-proportional energy by slowing down the
computation at lower datacenter loads based directly on response times (i.e.,
at lower loads, the proposal exploits the average slack in the time budget
provisioned for the peak load). In contrast, we propose TimeTrader to reduce
energy by exploiting the latency slack in the sub- critical replies which
arrive before the deadline (e.g., 80% of replies are 3-4x faster than the
tail). This slack is present at all loads and subsumes the previous work's
load-related slack. While the previous work shifts the leaves' response time
distribution to consume the slack at lower loads, TimeTrader reshapes the
distribution at all loads by slowing down individual sub-critical nodes without
increasing missed deadlines. TimeTrader exploits slack in both the network and
compute budgets. Further, TimeTrader leverages Earliest Deadline First
scheduling to largely decouple critical requests from the queuing delays of
sub- critical requests which can then be slowed down without hurting critical
requests. A combination of real-system measurements and at-scale simulations
shows that without adding to missed deadlines, TimeTrader saves 15-19% and
41-49% energy at 90% and 30% loading, respectively, in a datacenter with 512
nodes, whereas previous work saves 0% and 31-37%.Comment: 13 page
Design Space Exploration and Resource Management of Multi/Many-Core Systems
The increasing demand of processing a higher number of applications and related data on computing platforms has resulted in reliance on multi-/many-core chips as they facilitate parallel processing. However, there is a desire for these platforms to be energy-efficient and reliable, and they need to perform secure computations for the interest of the whole community. This book provides perspectives on the aforementioned aspects from leading researchers in terms of state-of-the-art contributions and upcoming trends
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