Inspired by the renaissance of optical computing recently, this poster presents a disruptive outlook on the possibility of seamless integration between optical communications and optical computing infrastructures, paving the way for achieving optical-layer intelligence and consequently boosting the capacity efficiency. This entails a paradigm shift in optical node architecture from the currently used optical-bypass to a novel one, entitled, optical-computing-enabled mode, where in addition to the traditional add-drop and cross-connect functionalities, optical nodes are upgraded to account for optical-computing capabilities between the lightpath entities directly at the optical layer. A preliminary study focusing on the optical aggregation operation is examined and early simulation results indicate a promising spectral saving enabled by the optical-computing-enabled mode compared with the optical-bypass one.

Unexpected long query latency of a database system can cause domino effects on all the upstream services and severely degrade end users' experience with unpredicted long waits, resulting in an increasing number of users disengaged with the services and thus leading to a high user disengagement ratio (UDR). A high UDR usually translates to reduced revenue for service providers. This paper proposes UTSLO, a UDR-oriented SLO guaranteed system, which enables a database system to support multi-tenant UDR targets in a cost-effective fashion through UDR-oriented capacity planning and dynamic UDR target enforcement. The former aims to estimate the feasibility of UDR targets while the latter dynamically tracks and regulates per-connection query latency distribution needed for accurate UDR target guarantee. In UTSLO, the database service capacity can be fully exploited to efficiently accommodate tenants while minimizing resources required for UDR target guarantee.

Modern database systems suffer from compromised throughput, persistent unfair I/O processing and unpredictable, high latency variability of user requests as a result of mismatches between highly scaled user parallel I/O and the I/O capacity afforded by the database and its underlying storage I/O stack. To address this problem, we introduce an efficient user-centric QoS-aware scheduling shim, called AppleS, for user-level fine-grained I/O regulation that delivers the right amount and pattern of user parallel I/O requests to the database system and supports user SLOs with high-level performance isolation and reduced I/O resource contention. It is designed to enable database systems to proactively regulate user request behaviors based on runtime conditions to reshape user access pattern to hide excessive user parallelism from the I/O stack that has a limited concurrent processing capability. This helps achieve scalable throughput for multi-user workloads in a fair and stable manner. AppleS is implemented as a user-space shim for transparent user-differentiated I/O scheduling, making it highly flexible and portable. Our extensive evaluation, run on real databases (MySQL and MongoDB), demonstrates that, by incorporating AppleS in the existing database systems, our solution can not only improve the throughput (up to 39.2%) in a fairer (3.2× to 40.6× fairness improvement) and more stable (up to 2× lower latency variability) manner, but also support user SLOs with less I/O provisioning.

Today s datacenters need to meet service level objectives (SLOs) for applications, which can be translated into deadlines for (co)flows running between job execution stages. As a result, meeting (co)flow deadlines with high probabilities is essential to attract and retain customers and hence, generate high revenue. To fill the lack of a transport protocol that can facilitate low (co)flow deadline miss rate, especially in the face of incast congestion, in this paper, we propose DCMRG, an incast-coflow-aware, ECN-based soft minimum-rate-guaranteed congestion control protocol for datacenter applications. DCMRG is composed of two major components, i.e., a congestion controller running on the send host and an incast congestion controller running on the receive host. DCMRG possesses three salient features. First, it is the first congestion control protocol that integrates congestion control with coflow-aware incast control while providing soft minimum flow rate guarantee. Second, DCMRG is readily deployable in datacenter networks. It only requires software upgrade in the hosts and minimum assistance (i.e., ECN) from in-network nodes. Third, DCMRG is backward compatible with and, by design, friendly to the widely deployed, standard-based transport protocols, such as DCTCP. The results from large-scale datacenter network simulation demonstrate that in the absence of incast congestion, DCMRG can reduce flow deadline miss rates by 3x and 1.6x compared to D2TCP and MRG, respectively. Moreover, DCMRG further reduces the coflow deadline miss rate by more than 40% and 60% and lowers the packet drop probability by 60% and 80%, in the face of incast congestion, compared to D2TCP with ICTCP and MRG with ICTCP, respectively.

The workflows of the predominant datacenter services are underlaid by various Fork-Join structures. Due to the lack of good understanding of the performance of Fork-Join structures in general, today's datacenters often operate under low resource utilization to meet stringent service level objectives (SLOs), e.g., in terms of tail and/or mean latency, for such services. Hence, to achieve high resource utilization, while meeting stringent SLOs, it is of paramount importance to be able to accurately predict the tail and/or mean latency for a broad range of Fork-Join structures of practical interests. In this article, we propose a black-box Fork-Join model that covers a wide range of Fork-Join structures for the prediction of tail and mean latency, called ForkTail and ForkMean, respectively. We derive highly computational effective, empirical expressions for tail and mean latency as functions of means and variances of task response times. Our extensive testing results based on model-based and trace-driven simulations, as well as a real-world case study in a cloud environment demonstrate that the models can consistently predict the tail and mean latency within 20 and 15 percent prediction errors at 80 and 90 percent load levels, respectively, for heavy-tailed workloads, and at any load levels for light-tailed workloads. Moreover, our sensitivity analysis demonstrates that such errors can be well compensated for with no more than 7 percent resource overprovisioning. Consequently, the proposed prediction model can be used as a powerful tool to aid the design of tail-and-mean-latency guaranteed job scheduling and resource provisioning, especially at high load, for datacenter applications.

Today's data center applications are predominantly data-intensive, calling for scaling out the workload to a large number of servers for parallel processing. Unfortunately, the existing scaling laws, notably, Amdahl's and Gustafson's laws are inadequate to characterize the scaling properties of dataintensive workloads. To fill this void, in this paper, we put forward a new scaling model, called In-Proportion and Scale-Out-induced scaling model (IPSO). IPSO generalizes the existing scaling models in two important aspects. First, it accounts for the possible in-proportion scaling, i.e., the scaling of the serial portion of the workload in proportion to the scaling of the parallelizable portion of the workload. Second, it takes into account the possible scaleout-induced scaling, i.e., the scaling of the collective overhead or workload induced by scaling out. IPSO exposes scaling properties of data-intensive workloads, rendering the existing scaling laws its special cases. In particular, IPSO reveals two new pathological scaling properties. Namely, the speedup may level off even in the case of the fixed-time workload underlying Gustafson's law, and it may peak and then fall as the system scales out. Extensive MapReduce and Spark-based case studies demonstrate that IPSO successfully captures diverse scaling properties of dataintensive applications. As a result, it can serve as a diagnostic tool to gain insights on or even uncover counter-intuitive root causes of observed scaling behaviors, especially pathological ones, for data-intensive applications. Finally, preliminary results also demonstrate the promising prospects of IPSO to facilitate effective resource provisioning to achieve the best speedup-versuscost tradeoffs for data-intensive applications.

The workflows of the predominant user-facing datacenter services, including web searching and social networking, are underlaid by various Fork-Join structures. Due to the lack of understanding the performance of Fork-Join structures in general, today's datacenters often resort to resource overprovisioning, operating under low resource utilization, to meet stringent tail-latency service level objectives (SLOs) for such services. Hence, to achieve high resource utilization, while meeting stringent tail-latency SLOs, it is of paramount importance to be able to accurately predict the tail latency for a broad range of Fork-Join structures of practical interests.

Mass spectrometry (MS) has been used to generate protein profiles from human serum, and proteomic data obtained from MS have attracted great interest for the detection of cancer. Because MALDI-TOF MS provides high-resolution measurements, the biomarker identification has been limited by the unbalance problem between high- dimensional attributes and small sample-size. To deal with the multi-class problem in cancer prediction and biomarker identification, we propose a fast and robust multi-class cancer classification framework. A novel MS biomarker selection algorithm is provided by utilizing oversampled wavelet transform to extract wavelet coefficients and statistical testing to select features. The multi-class Gentle AdaBoost is used as a classifier due to its efficient classification procedure. Several experiments are deployed on real MALDI-TOF MS data in order to prove the superiority of proposed method compared to previous algorithms. The experimental results show that our proposed framework is an effective tool for analyzing MS data in cancer detection.